| Course Name | Code | Semester | T+U Hours | Credit | ECTS |
|---|---|---|---|---|---|
| Analysis Of The Limited Dependent Variable | EKO 570 | 0 | 3 + 0 | 3 | 6 |
| Precondition Courses | |
| Recommended Optional Courses | |
| Course Language | Turkish |
| Course Level | yuksek_lisans |
| Course Type | Optional |
| Course Coordinator | Dr.Öğr.Üyesi SAYIN SAN |
| Course Lecturers | Dr.Öğr.Üyesi SAYIN SAN, |
| Course Assistants | |
| Course Category | Available Basic Education in the Field |
| Course Objective | This course examines several types of advanced regression models that are frequently used in policy analysis and social science research. The key similarity of these models is that they involve dependent variables that violate one or more of the assumptions of the Ordinary Least Squares (OLS) regression model. |
| Course Content | The main models examined in the course are binary logit and probit, multinomial logit, ordinal probit, tobit, and the family of Poisson regression models. All these models are estimated using maximum likelihood estimation (MLE). The Heckman correction for selection is also addressed. |
| # | Course Learning Outcomes | Teaching Methods | Assessment Methods |
|---|---|---|---|
| 1 | Analyze choice microdata using statistical packages | Lecture, Group Study, Case Study, | Testing, Homework, Project / Design, |
| 2 | Interpret the results of model estimation and prediction | Case Study, Group Study, Lecture, | Project / Design, Homework, Testing, Performance Task, |
| 3 | Summarize the empirical implications of the statistical assumptions of regression models with limited dependent variables | Group Study, Lecture, | Homework, Testing, |
| 4 | Formulate and test behavioural hypotheses | Case Study, Lecture, | Homework, Testing, |
| Week | Course Topics | Preliminary Preparation |
|---|---|---|
| 1 | From regression to conditional probability models | |
| 2 | Estimation and statistical tests | |
| 3 | Introduction to binary choice | |
| 4 | Maximum likelihood estimator | |
| 5 | Binary logit and probit models | |
| 6 | Probit models | |
| 7 | Mutinomial probit model | |
| 8 | MIDTERM | |
| 9 | Conditional and multinomial logit models | |
| 10 | Bayes estimator of the mixed logit model | |
| 11 | Discrete Outcomes | |
| 12 | Censored Outcomes | |
| 13 | Truncated Outcomes | |
| 14 | FINAL |
| Resources | |
|---|---|
| Course Notes | Microeconometrics using STATA, (2009). Cameron, Colin A. and Pratin K. Trivedi, STATA Publication.<br><br>Analysis of Micro Data, (2006). Winkellman, Rainer and Stefan Boes, Springer Publication.<br><br>Regression Models for Categorical Dependent Variables using STATA, (2001). Long J. Scott, STATA Press |
| Course Resources | |
| Order | Program Outcomes | Level of Contribution | |||||
|---|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | |||
| 1 | Economic and financial issues in conjunction with the expansion and deepening of information by econometric analysis information to evaluate, interpret and apply | X | |||||
| 2 | Using the theoretical knowledge in finance and economics combine the ability to apply scientific methods and knowledge of econometric methods to analyze and interpret | X | |||||
| 3 | Problems experienced in the financial sector to analyze with scientific methods and solutions development | X | |||||
| 4 | The oral and visual way to transfer to Current developments in the financial sector, for the financial sector and outside groups as written | X | |||||
| 5 | Both public as well as how it should be in the private sector require highly skilled personnel; have the theoretical knowledge and practical advanced analysis capabilities in economic and financial matters | X | |||||
| 6 | At least one foreign language spoken and written communication having the ability ("European Language Portfolio Global Scale", Level B2 | X | |||||
| 7 | they internalize the knowledge and problem-solving skills, ability to apply interdisciplinary studies | X | |||||
| Evaluation System | |
|---|---|
| Semester Studies | Contribution Rate |
| 1. Ödev | 20 |
| 2. Ödev | 20 |
| 3. Ödev | 20 |
| 4. Ödev | 20 |
| 5. Ödev | 20 |
| Total | 100 |
| 1. Yıl İçinin Başarıya | 50 |
| 1. Final | 50 |
| Total | 100 |
| ECTS - Workload Activity | Quantity | Time (Hours) | Total Workload (Hours) |
|---|---|---|---|
| Course Duration (Including the exam week: 16x Total course hours) | 16 | 3 | 48 |
| Hours for off-the-classroom study (Pre-study, practice) | 16 | 5 | 80 |
| Mid-terms | 1 | 3 | 3 |
| Assignment | 2 | 3 | 6 |
| Project / Design | 3 | 3 | 9 |
| Final examination | 1 | 4 | 4 |
| Total Workload | 150 | ||
| Total Workload / 25 (Hours) | 6 | ||
| dersAKTSKredisi | 6 | ||